Abstract
Assessment of physiological instability preceding adverse events on hospital wards has been previously investigated through clinical early warning score systems. Early warning scores are simple to use yet they consider data as independent and identically distributed random variables. Deep learning applications are able to learn from sequential data, however they lack interpretability and are thus difficult to deploy in clinical settings. We propose the 'Deep Early Warning System' (DEWS), an interpretable end-to-end deep learning model that interpolates temporal data and predicts the probability of an adverse event, defined as the composite outcome of cardiac arrest, mortality or unplanned ICU admission. The model was developed and validated using routinely collected vital signs of patients admitted to the the Oxford University Hospitals between 21st March 2014 and 31st March 2018. We extracted 45 314 vital-sign measurements as a balanced training set and 359 481 vital-sign measurements as an imbalanced testing set to mimic a real-life setting of emergency admissions. DEWS achieved superior accuracy than the state-of-the-art that is currently implemented in clinical settings, the National Early Warning Score, in terms of the overall area under the receiver operating characteristic curve (AUROC) (0.880 vs. 0.866) and when evaluated independently for each of the three outcomes. Our attention-based architecture was able to recognize 'historical' trends in the data that are most correlated with the predicted probability. With high sensitivity, improved clinical utility and increased interpretability, our model can be easily deployed in clinical settings to supplement existing EWS systems.
Highlights
I N RECENT years, increased access to Electronic Health Records (EHR) has motivated the development of datadriven systems that detect physiological derangement to secure timely response
Unlike currently implemented Early Warning Score (EWS) systems that were originally designed in a heuristic fashion, we developed and validated an interpretable end-to-end Deep Early Warning System (DEWS) that alerts for clinical deterioration, defined as the composite outcome of unplanned ICU admission, mortality, and cardiac arrest
We propose an attention-based neural network that learns from historical trends of vital signs through interpolated mean and variance features to alert for clinical deterioration
Summary
I N RECENT years, increased access to Electronic Health Records (EHR) has motivated the development of datadriven systems that detect physiological derangement to secure timely response. Warning Score (EWS) systems assess a patient’s degree of illness by assigning scores to routinely collected vital-sign measurements based on pre-determined normality ranges. The National Early Warning Score (NEWS), which is currently used in hospitals and recommended by the Royal College of Physicians in the United Kingdom [1], has shown superior performance in comparison to other EWS systems in detecting the composite outcome of unplanned ICU admission, cardiac arrest, and mortality [2]. EWS systems assign an independent score to each vital-sign variable and assume that vital-sign measurements are independent and identically distributed (I.I.D.) random variables. Given their simplistic nature, traditional EWS systems do not learn any spatio-temporal information from the vital signs. We hypothesized that the use of deep learning may improve the accuracy of predicting clinical outcomes by recognizing complex patterns in the data
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